Introduction
In this study, authors Megan E. Piper, Wei-Yin Loh, Stevens
S. Smith, Sandra J. Japuntich, and Timothy B. Baker used decision tree analysis
to identify how variables linked to relapse of smoking interact with each other.
The researchers then used this
information to identify subgroups of smokers at a higher risk for relapse, and
compared the results to those found through traditional methods.
Summary
Researchers conducted the study by inserting 70 variables
with a connection to relapse in smoking, such as predictors related to
environmental factors, dependence, and smoking history, into a decision tree model.
This model is designed to take into account the effects of multiple factors. The
more traditional model, logistic regression analysis, is a good predictor of
factors influential to large groups and was used in addition to decision trees
in order to create a comparison between the methods. Previous studies that
depended solely on linear or logistic regression techniques were unable to identify
smaller subgroups. Decision tree
analysis allowed researchers to divide the data sets in order to look at the
effects of certain factors on specific groups, such as men and women.
This study used the GUIDE decision tree program to analyze
results from 1,071 smokers. The
researchers designed models for one week after quitting, end of treatment, and
six months postquit. The figure below shows the “pruned” decision tree of the
question “How soon after you wake up do you smoke your first cigarette?” From
the results of this question, taken one week postquit, the decision tree splits
further to show the abstinence rates of smokers who did or did not receive
treatment, and those who are or are not married. The study showed that people
who smoke their first cigarette more than 30 minutes after waking are the most
likely to quit smoking, and that it is important to give treatment to those
whose first cigarette is less than 30 minutes after waking up.
The second figure shows how marital status, gender, and the
age began daily smoking interacted to affect abstinence at the end of
treatment. Household income, health status, and longest previous quit attempt
were also significant factors identified to influence successful cessation of
smoking. The results showed that it is important to consider environmental and contextual factors in addition to those related to dependence.
Although both the decision tree analysis and logistic
regression technique used the same data, they produced significantly different
results. According to the authors, the decision tree model was better suited to
handling a large number of factors; however, they were not able to determine
which method is more accurate. Overall,
decision tree analysis produced statistically significant results that will
hopefully be replicated in subsequent studies.
Conclusion
This study is a very good example of how decision tree analysis can be applied in real world situations to predict human behavior. One important characteristic of decision trees is that although they are based on scientific calculations, they are easy to understand and could be shown to decision makers to help explain the results of the analysis. Although this case study uses quantitative data, it would be interesting to see how decision trees can be translated for use with qualitative data more commonly used for intelligence analysis.
Source
Piper, M.E., Wei-Yin, L., Stevens, S.S., Japuntich, S.J.,
Baker, T.B. (2011). Using decision tree analysis to identify risk factors for relapse to smoking. Substance Use & Misuse 46, p.
492-510. doi: 10.3109/10826081003682222
I agree that this article is very interesting, and I'm wondering if the study can be taken a step farther to determine what factors leads someone to begin to use illegal substances. Since the study could predict those who would relapse I would find it very useful to predict what factors leads individuals to begin to use in the first place.
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